.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_explore.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_explore.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_explore.py:


Exploring data
==============

Explores the Tecator data set by plotting the functional data and calculating
means and derivatives.

.. GENERATED FROM PYTHON SOURCE LINES 8-16

.. code-block:: Python


    # Author: Miguel Carbajo Berrocal
    # License: MIT

    import numpy as np

    import skfda








.. GENERATED FROM PYTHON SOURCE LINES 17-24

In this example we are going to explore the functional properties of the
:func:`Tecator <skfda.datasets.fetch_tecator>` dataset. This dataset
measures the infrared absorbance spectrum of meat samples. The objective is
to predict the fat, water, and protein content of the samples.

In this example we only want to discriminate between meat with less than 20%
of fat, and meat with a higher fat content.

.. GENERATED FROM PYTHON SOURCE LINES 24-28

.. code-block:: Python

    X, y = skfda.datasets.fetch_tecator(return_X_y=True, as_frame=True)
    fd = X.iloc[:, 0].values
    fat = y['fat'].values








.. GENERATED FROM PYTHON SOURCE LINES 29-31

We will now plot in red samples containing less than 20% of fat and in blue
the rest.

.. GENERATED FROM PYTHON SOURCE LINES 31-48

.. code-block:: Python


    low_fat = fat < 20
    labels = np.full(fd.n_samples, 'high fat')
    labels[low_fat] = 'low fat'
    colors = {
        'high fat': 'red',
        'low fat': 'blue',
    }

    fig = fd.plot(
        group=labels,
        group_colors=colors,
        linewidth=0.5,
        alpha=0.7,
        legend=True,
    )




.. image-sg:: /auto_examples/images/sphx_glr_plot_explore_001.png
   :alt: Spectrometric curves
   :srcset: /auto_examples/images/sphx_glr_plot_explore_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 49-50

The means of each group are the following ones.

.. GENERATED FROM PYTHON SOURCE LINES 50-64

.. code-block:: Python


    mean_low = skfda.exploratory.stats.mean(fd[low_fat])
    mean_high = skfda.exploratory.stats.mean(fd[~low_fat])

    means = mean_high.concatenate(mean_low)

    means.dataset_name = f"{fd.dataset_name} - means"
    means.plot(
        group=['high fat', 'low fat'],
        group_colors=colors,
        linewidth=0.5,
        legend=True,
    )




.. image-sg:: /auto_examples/images/sphx_glr_plot_explore_002.png
   :alt: Spectrometric curves - means
   :srcset: /auto_examples/images/sphx_glr_plot_explore_002.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 640x480 with 1 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 65-71

In this dataset, the vertical shift in the original trajectories is not
very significative for predicting the fat content. However, the shape of the
curve is very relevant. We can observe that looking at the first and second
derivatives.

The first derivative is shown below:

.. GENERATED FROM PYTHON SOURCE LINES 71-82

.. code-block:: Python


    fdd = fd.derivative()
    fdd.dataset_name = f"{fd.dataset_name} - derivative"
    fig = fdd.plot(
        group=labels,
        group_colors=colors,
        linewidth=0.5,
        alpha=0.7,
        legend=True,
    )




.. image-sg:: /auto_examples/images/sphx_glr_plot_explore_003.png
   :alt: Spectrometric curves - derivative
   :srcset: /auto_examples/images/sphx_glr_plot_explore_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 83-84

We now show the second derivative:

.. GENERATED FROM PYTHON SOURCE LINES 84-93

.. code-block:: Python

    fdd = fd.derivative(order=2)
    fdd.dataset_name = f"{fd.dataset_name} - second derivative"
    fig = fdd.plot(
        group=labels,
        group_colors=colors,
        linewidth=0.5,
        alpha=0.7,
        legend=True,
    )



.. image-sg:: /auto_examples/images/sphx_glr_plot_explore_004.png
   :alt: Spectrometric curves - second derivative
   :srcset: /auto_examples/images/sphx_glr_plot_explore_004.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 2.418 seconds)


.. _sphx_glr_download_auto_examples_plot_explore.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/GAA-UAM/scikit-fda/develop?filepath=examples/plot_explore.py
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_explore.ipynb <plot_explore.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_explore.py <plot_explore.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_